The Practitioner's Guide to Graph Data

Chapter 1. Graph Thinking

Think about the first time you learned about graph technology.

The scene probably started at the whiteboard where your team of directors, architects, scientists, and engineers were discussing your data problems. Eventually, someone drew the connections from one piece of data to another. After stepping back, someone noted that the links across the data built up a graph.

That realization sparked the beginning of your team’s graph journey. The group saw that you could use relationships across the data to provide new and powerful insights to the business. An individual or a small group was probably tasked with evaluating the techniques and tools available for storing, analyzing, and/or retrieving graph-shaped data.

The next major revelation for your team was likely that it’s easy to explain your data as a graph. But it’s hard to use your data as a graph.

Sound familiar?

Much like this whiteboard experience, earlier teams discovered connections within their data and turned them into valuable applications we use everyday. Think about apps like Netflix, LinkedIn, and GitHub. These products translate connected data into an integral asset used by millions of people around the world.

We wrote this book to teach you how they did it.

As both tool builders and tool users, we have had the opportunity to sit on both sides of the whiteboard conversation hundreds of times. From our experiences, we collected a core set of choices and subsequent technology decisions to ...